Checkpoint 1: EDA

By Arturo, Payson, and Victoria

Does healthcare access affect the number of preventable hospital stays of certain racial groups at the county level?

Why this matters:

• Reduce preventable hospitalizations
• Equitable resource allocation
• Focus efforts where help is needed most

National Data

Source: County Health Rankings 2025

Raw Data:

• County-level health outcomes(ex)
• Healthcare access variables(ex)
• Disaggregated by race

Cleaned Data:

• Filtered and cleaned to complete observations
• Created “White Majority” vs. “Minority” indicator

Preventable Hospitalizations by County

Regression Analysis of Racial Disparities in Preventable Hospital Stays

Planned Approach:

• Linear regression with interaction terms
• Outcome: Preventable hospital stays
• Predictors: Uninsured rate, provider ratio, race group

Modeling Strategy:

• Explore variable selection and regularization

Moving into modeling and interpretation

Completed:

• Defined research question
• Cleaned and preprocessed dataset
• Created initial EDA visualizations

Next Steps:

• Fit regression models with race × access interactions
• Interpret disparities and validate model
• Create visual summaries and poster